The importance of recognizing the projection view of radiographs includes the following two aspects. Firstly, it can help automate the image rendering procedure and optimize the image display quality. According to the workflow of a Computer Radiograph (CR) system, a technologist takes radiographs ordered in an examination, and then scans each CR cassettes while manually typing in the projection view associated with the cassette. This projection view information, together with the body part information which is obtained when the examination is ordered, determine the characteristics of the radiograph and directly influence the choice of image rendering parameters. Therefore, the success of recognizing the projection view of radiograph can help eliminate the need of the radiologist input, automate the image rendering process, and expedite the workflow. Secondly, projection view recognition can also benefit image management in Picture Archiving and Communication Systems (PACS). For example, if the projection view information is derived automatically from the image contents, it could reduce the occurrence of missing or incorrect information in image header and make the medical image management system in PACS more rational and efficient.
However, recognizing the projection view of radiographs is a challenging problem as radiographs are often taken under a variety of examination condition. The patient's pose and size could be variant; so is the preference of radiologist depending on the patient's situation. All these factors would cause radiographs from the same examination to appear quite different. Human beings tend to use high level semantics to identify the projection view of a radiograph by capturing the image contents, grouping them into meaningful objects and matching them with contextual information (i.e. a medical exam). However all these analysis procedures are difficult for computer to achieve in a similar fashion due to the limitation of the image analysis algorithms.
Some attempts have been made toward projection view recognition of medical images. For example, I. Kawshita et. al. (“Development of Computerized Method for Automated Classification of Body Parts in Digital Radiographs”, RSNA 2002) present a method to classify six body parts. The method examines the similarity of a given image to a set of pre-determined template images by using the cross-correlation values as the similarity measure. However, the manual generation of these template images is quite time consuming, and more particularly, it is highly observer dependent, which may introduce error into the classification. Guld et. al. (“Comparison of Global Features for Categorization of Medical Images”, SPIE medical Imaging 2004) discloses a method to evaluate a set of global features extracted from images for classification. In both methods, no preprocessing is implemented to reduce the influence of irrelevant and often distracting data. For example, the unexposed regions caused by the blocking of the x-ray collimator during the exposure may result in a significant white borders surrounding the image. Applicants have noted that if such regions are not removed in a pre-processing step and therefore used in the computation of similarity measures, the classification results can be seriously biased.
Recent literature focuses on natural scene image classification. Examples include QBIC (W. Niblack, et al, “The QBIC project: Querying images by content using color, texture, and shape” Proc. SPIE Storage and Retrieval for Image and Video Databases, February 1994), Photobook (A. Pentland, et. al. “Photobook: Content-based manipulation of image database”. International Journal of Computer Vision, 1996), Virage (J. R. Bach, et al. “The Virage image search engine: An open framework for image management” Proc. SPIE Storage and Retrieval for image and Video Database, vol 2670, pp. 76-97, 1996), Visualseek (R. Smith, et al. “Visualseek: A fully automated content-based image query system” Proc ACM Multimedia 96, 1996), Netra (Ma, et al. “Netra: A toolbox for navigating large image databases” Proc IEEE Int. Conf. On Image Proc. 1997), and MAR (T. S. Huang, et. al, “Multimedia analysis and retrieval system (MARS) project” Proc of 33rd Annual Clinic on Library Application of Data Processing Digital Image Access and Retrieval, 1996). These systems follow the same computational paradigm which treats an image as a whole entity and represents it via a set of low-level features or attributes, such as color, texture, shape and layout. Typically, these feature attributes together form a feature vector and image classification based on clustering these low-level visual feature vectors. In many cases, the most effective feature is color. However, the color information is not available in radiographs. Therefore these methods are not directly suitable for radiograph projection view recognition.
Given the limitations of the prior art, there exists a need for a method to automatically recognize the projection view of radiographs. Such a method should be robust enough to handle large variations in radiographs.